{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node2Vec representation learning with Stellargraph components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/embeddings/keras-node2vec-embeddings.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/embeddings/keras-node2vec-embeddings.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example demonstrates how to apply components from the stellargraph library to perform representation learning via Node2Vec. This uses a Keras implementation of Node2Vec available in stellargraph instead of the reference implementation provided by ``gensim``. This implementation provides flexible interfaces to downstream tasks for end-to-end learning.\n",
    "\n",
    "<a name=\"refs\"></a>\n",
    "**References**\n",
    "\n",
    "[1] Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. ([link](https://snap.stanford.edu/node2vec/))\n",
    "\n",
    "[2] Distributed representations of words and phrases and their compositionality. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean.  In Advances in Neural Information Processing Systems (NIPS), pp. 3111-3119, 2013. ([link](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf))\n",
    "\n",
    "[3] word2vec Parameter Learning Explained. X. Rong. arXiv preprint arXiv:1411.2738. 2014 Nov 11. ([link](https://arxiv.org/pdf/1411.2738.pdf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Following word2vec [2,3], for each (``target``,``context``) node pair $(v_i,v_j)$ collected from random walks,  we learn the representation for the target node $v_i$ by using it to predict the existence of context node $v_j$, with the following three-layer neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](word2vec-illustration.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Node $v_i$'s representation in the hidden layer is obtained by multiplying $v_i$'s one-hot representation in the input layer with the input-to-hidden weight matrix $W_{in}$, which is equivalent to look up the $i$th row of input-to-hidden weight matrix $W_{in}$. The existence probability of each node conditioned on node $v_i$ is outputted in the output layer, which is obtained by multiplying $v_i$'s hidden-layer representation with the hidden-to-out weight matrix $W_{out}$ followed by a softmax activation. To capture the ``target-context`` relation between $v_i$ and $v_j$, we need to maximize the probability $\\mathrm{P}(v_j|v_i)$. However, computing $\\mathrm{P}(v_j|v_i)$ is time consuming, which involves the matrix multiplication between $v_i$'s hidden-layer representation and the hidden-to-out weight matrix $W_{out}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To speed up the computing, we adopt the negative sampling strategy [2,3]. For each (``target``, ``context``) node pair, we sample a negative node $v_k$, which is not $v_i$'s context. To obtain the output, instead of multiplying $v_i$'s hidden-layer representation with the hidden-to-out weight matrix $W_{out}$ followed by a softmax activation, we only calculate the dot product between $v_i$'s hidden-layer representation and the $j$th column as well as the $k$th column of the hidden-to-output weight matrix $W_{out}$ followed by a sigmoid activation respectively. According to [3], the original objective to maximize $\\mathrm{P}(v_j|v_i)$ can be approximated by minimizing the cross entropy between $v_j$ and $v_k$'s outputs and their ground-truth labels (1 for $v_j$ and 0 for $v_k$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following [2,3], we denote the rows of the input-to-hidden weight matrix $W_{in}$ as ``input_embeddings`` and the columns of the hidden-to-out weight matrix $W_{out}$ as ``output_embeddings``. To build the Node2Vec model, we need look up ``input_embeddings`` for target nodes and ``output_embeddings`` for context nodes and calculate their inner product together with a sigmoid activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import os\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "\n",
    "from stellargraph import StellarGraph\n",
    "from stellargraph.data import BiasedRandomWalk\n",
    "from stellargraph.data import UnsupervisedSampler\n",
    "from stellargraph.data import BiasedRandomWalk\n",
    "from stellargraph.mapper import Node2VecLinkGenerator, Node2VecNodeGenerator\n",
    "from stellargraph.layer import Node2Vec, link_classification\n",
    "\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset\n",
    "\n",
    "\n",
    "For clarity, we use only the largest connected component, ignoring isolated nodes and subgraphs; having these in the data does not prevent the algorithm from running and producing valid results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "G, subjects = dataset.load(largest_connected_component_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 2485, Edges: 5209\n",
      "\n",
      " Node types:\n",
      "  paper: [2485]\n",
      "    Features: float32 vector, length 1433\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5209]\n",
      "        Weights: all 1 (default)\n",
      "        Features: none\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Node2Vec algorithm\n",
    "\n",
    "The Node2Vec algorithm introduced in [[1]](#refs) is a 2-step representation learning algorithm. The two steps are:\n",
    "\n",
    "1. Use random walks to generate sentences from a graph. A sentence is a list of node ids. The set of all sentences makes a corpus.\n",
    "\n",
    "2. The corpus is then used to learn an embedding vector for each node in the graph. Each node id is considered a unique word/token in a dictionary that has size equal to the number of nodes in the graph. The Word2Vec algorithm [[2]](#refs) is used for calculating the embedding vectors.\n",
    "\n",
    "In this implementation, we train the Node2Vec algorithm in the following two steps:\n",
    "\n",
    "1. Generate a set of (`target`, `context`) node pairs through starting the biased random walk with a fixed length at per node. The starting nodes are taken as the target nodes and the following nodes in biased random walks are taken as context nodes. For each (`target`, `context`) node pair, we generate 1 negative node pair.\n",
    "\n",
    "2. Train the Node2Vec algorithm through minimizing cross-entropy loss for `target-context` pair prediction, with the predictive value obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the optional parameter values: the number of walks to take per node, the length of each walk. Here, to guarantee the running efficiency, we respectively set `walk_number` and `walk_length` to 100 and 5. Larger values can be set to them to achieve better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "walk_number = 100\n",
    "walk_length = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the biased random walker to perform context node sampling, with the specified parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "walker = BiasedRandomWalk(\n",
    "    G,\n",
    "    n=walk_number,\n",
    "    length=walk_length,\n",
    "    p=0.5,  # defines probability, 1/p, of returning to source node\n",
    "    q=2.0,  # defines probability, 1/q, for moving to a node away from the source node\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the UnsupervisedSampler instance with the biased random walker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "unsupervised_samples = UnsupervisedSampler(G, nodes=list(G.nodes()), walker=walker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the batch size and the number of epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "epochs = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define an attri2vec training generator, which generates a batch of (index of target node, index of context node, label of node pair) pairs per iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = Node2VecLinkGenerator(G, batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the Node2Vec model, with the dimension of learned node representations set to 128."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_size = 128\n",
    "node2vec = Node2Vec(emb_size, generator=generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = node2vec.in_out_tensors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the link_classification function to generate the prediction, with the 'dot' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the ``input embedding`` of the target node and the ``output embedding`` of the context node followed by a sigmoid activation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "link_classification: using 'dot' method to combine node embeddings into edge embeddings\n"
     ]
    }
   ],
   "source": [
    "prediction = link_classification(\n",
    "    output_dim=1, output_act=\"sigmoid\", edge_embedding_method=\"dot\"\n",
    ")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack the Node2Vec encoder and prediction layer into a Keras model. Our generator will produce batches of positive and negative context pairs as inputs to the model. Minimizing the binary crossentropy between the outputs and the provided ground truth is much like a regular binary classification task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=prediction)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(lr=1e-3),\n",
    "    loss=keras.losses.binary_crossentropy,\n",
    "    metrics=[keras.metrics.binary_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 39760 steps\n",
      "Epoch 1/2\n",
      "39760/39760 [==============================] - 155s 4ms/step - loss: 0.2924 - binary_accuracy: 0.8557\n",
      "Epoch 2/2\n",
      "39760/39760 [==============================] - 238s 6ms/step - loss: 0.1096 - binary_accuracy: 0.9641\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    generator.flow(unsupervised_samples),\n",
    "    epochs=epochs,\n",
    "    verbose=1,\n",
    "    use_multiprocessing=False,\n",
    "    workers=4,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise Node Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the node based model for predicting node representations from node ids and the learned parameters. Below a Keras model is constructed, with `x_inp[0]` as input and `x_out[0]` as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp_src = x_inp[0]\n",
    "x_out_src = x_out[0]\n",
    "embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the node embeddings from node ids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "node_gen = Node2VecNodeGenerator(G, batch_size).flow(subjects.index)\n",
    "node_embeddings = embedding_model.predict(node_gen, workers=4, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform the embeddings to 2d space for visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = TSNE  # PCA\n",
    "\n",
    "trans = transform(n_components=2)\n",
    "node_embeddings_2d = trans.fit_transform(node_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw the embedding points, coloring them by the target label (paper subject)\n",
    "alpha = 0.7\n",
    "label_map = {l: i for i, l in enumerate(np.unique(subjects))}\n",
    "node_colours = [label_map[target] for target in subjects]\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.axes().set(aspect=\"equal\")\n",
    "plt.scatter(\n",
    "    node_embeddings_2d[:, 0],\n",
    "    node_embeddings_2d[:, 1],\n",
    "    c=node_colours,\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "plt.title(\"{} visualization of node embeddings\".format(transform.__name__))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downstream task\n",
    "\n",
    "The node embeddings calculated using Node2Vec can be used as feature vectors in a downstream task such as node attribute inference (e.g., inferring the subject of a paper in Cora), community detection (clustering of nodes based on the similarity of their embedding vectors), and link prediction (e.g., prediction of citation links between papers)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/embeddings/keras-node2vec-embeddings.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/embeddings/keras-node2vec-embeddings.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
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